Machine learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: a swiss pilot study.

BACKGROUND: Anastomotic leakage (AL), a severe complication following colorectal surgery, arises from defects at the anastomosis site. This study evaluates the feasibility of predicting AL using machine learning (ML) algorithms based on preoperative data. METHODS: We retrospectively analyzed data including 21 predictors from patients undergoing colorectal surgery with bowel anastomosis at four Swiss hospitals. Several ML algorithms were applied for binary classification into AL or non-AL groups, utilizing a five-fold cross-validation strategy with a 90% training and 10% validation split. Additionally, a holdout test set from an external hospital was employed to assess the models' robustness in external validation. RESULTS: Among 1244 patients, 112 (9.0%) suffered from AL. The Random Forest model showed an AUC-ROC of 0.78 (SD: ± 0.01) on the internal test set, which significantly decreased to 0.60 (SD: ± 0.05) on the external holdout test set comprising 198 patients, including 7 (3.5%) with AL. Conversely, the Logistic Regression model demonstrated more consistent AUC-ROC values of 0.69 (SD: ± 0.01) on the internal set and 0.61 (SD: ± 0.05) on the external set. Accuracy measures for Random Forest were 0.82 (SD: ± 0.04) internally and 0.87 (SD: ± 0.08) externally, while Logistic Regression achieved accuracies of 0.81 (SD: ± 0.10) and 0.88 (SD: ± 0.15). F1 Scores for Random Forest moved from 0.58 (SD: ± 0.03) internally to 0.51 (SD: ± 0.03) externally, with Logistic Regression maintaining more stable scores of 0.53 (SD: ± 0.04) and 0.51 (SD: ± 0.02). CONCLUSION: In this pilot study, we evaluated ML-based prediction models for AL post-colorectal surgery and identified ten patient-related risk factors associated with AL. Highlighting the need for multicenter data, external validation, and larger sample sizes, our findings emphasize the potential of ML in enhancing surgical outcomes and inform future development of a web-based application for broader clinical use.

A randomized controlled trial of social media promotion in neurosurgical publishing.

The importance of social media has seen a dramatic increase in recent times, but much about its influence in academia is still unknown. To date, no comparative studies analysing the effect of social media promotion on citation counts have been undertaken in neurosurgical publishing. We randomized 177 articles published in Acta Neurochirurgica from May to September 2020. The 89 articles in the intervention group received a standardized social media promotion through one post on our official Twitter/X account, whereas the 88 articles in the control group did not receive any social media promotion. Citation counts, website visits and PDF downloads were tracked at one and two years post-promotion. We found no significant difference in number of citations at one year post-promotion (Intervention: 1.85 ± 3.94 vs. Control: 2.67 ± 6.65, p = 0.322) or at two years (5.35 ± 7.39 vs. 7.09 ± 12.1, p = 0.249). Similarly, no difference was detected in website visits at one (587.46 ± 568.04 vs. 590.65 ± 636.25, p = 0.972) or two years (865.79 ± 855.80 vs. 896.31 ± 981.97, p = 0.826) and PDF downloads at one (183.40 ± 152.02 vs. 187.78 ± 199.01, p = 0.870) or two years (255.99 ± 218.97 vs. 260.97 ± 258.44, p = 0.890). In a randomized study, a structured promotion of general neurosurgical articles on Twitter/X did not significantly impact citation count, website visits, or PDF downloads compared to no social media promotion. Combined with published evidence to date, the impact of social media on citation counts in academic publishing ultimately remains unclear.

Predictive value of the five-repetition sit-to-stand test for outcomes after surgery for lumbar disc herniation: prospective study.

OBJECTIVE: It is unknown whether presence of pre-operative objective functional impairment (OFI) can predict post-operative outcomes in patients with lumbar disc herniation (LDH). We aimed to determine whether pre-operative OFI measured by the five-repetition sit-to-stand test (5R-STS) could predict outcomes at 12-months post-discectomy. METHODS: Adult patients with LDH scheduled for surgery were prospectively recruited from a Dutch short-stay spinal clinic. The 5R-STS time and patient reported outcome measures (PROMs) including Oswestry Disability Index, Roland-Morris Disability Questionnaire, Visual Analogue Scale (VAS) for back and leg pain, EQ-5D-3L health-related quality of life, EQ5D-VAS and ability to work were recorded pre-operatively and at 12-months. A 5R-STS time cut-off of ≥ 10.5 s was used to determine OFI. Mann-Whitney and Chi-square tests were employed to determine significant differences in post-operative outcomes between groups stratified by presence of pre-operative OFI. RESULTS: We recruited 134 patients in a prospective study. Twelve-month follow-up was completed by 103 (76.8%) patients. Mean age was 53.2 ± 14.35 years and 50 (48.5%) patients were female. Pre-operatively, 53 (51.5%) patients had OFI and 50 (48.5%) did not. Post-operatively, patients with OFI experienced a significantly greater mean change (p < 0.001) across all PROMs compared to patients without OFI, except leg pain (p = 0.176). There were no significant differences in absolute PROMs between groups at 12-months (all p > 0.05). CONCLUSIONS: The presence of OFI based on 5R-STS time does not appear to decrease a patient's likelihood of experiencing satisfactory post-operative outcomes. The 5R-STS cannot predict how a patient with LDH will respond to surgery at 12-month follow-up.

Calculation of the minimum clinically important difference (MCID) using different methodologies: case study and practical guide.

INTRODUCTION: Establishing thresholds of change that are actually meaningful for the patient in an outcome measurement instrument is paramount. This concept is called the minimum clinically important difference (MCID). We summarize available MCID calculation methods relevant to spine surgery, and outline key considerations, followed by a step-by-step working example of how MCID can be calculated, using publicly available data, to enable the readers to follow the calculations themselves. METHODS: Thirteen MCID calculations methods were summarized, including anchor-based methods, distribution-based methods, Reliable Change Index, 30% Reduction from Baseline, Social Comparison Approach and the Delphi method. All methods, except the latter two, were used to calculate MCID for improvement of Zurich Claudication Questionnaire (ZCQ) Symptom Severity of patients with lumbar spinal stenosis. Numeric Rating Scale for Leg Pain and Japanese Orthopaedic Association Back Pain Evaluation Questionnaire Walking Ability domain were used as anchors. RESULTS: The MCID for improvement of ZCQ Symptom Severity ranged from 0.8 to 5.1. On average, distribution-based methods yielded lower MCID values, than anchor-based methods. The percentage of patients who achieved the calculated MCID threshold ranged from 9.5% to 61.9%. CONCLUSIONS: MCID calculations are encouraged in spinal research to evaluate treatment success. Anchor-based methods, relying on scales assessing patient preferences, continue to be the "gold-standard" with receiver operating characteristic curve approach being optimal. In their absence, the minimum detectable change approach is acceptable. The provided explanation and step-by-step example of MCID calculations with statistical code and publicly available data can act as guidance in planning future MCID calculation studies.

Identifying clusters of objective functional impairment in patients with degenerative lumbar spinal disease using unsupervised learning.

OBJECTIVES: The five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment (OFI), and thus provides an adjunctive dimension in patient assessment. It is conceivable that there are different subsets of patients with OFI and degenerative lumbar disease. We aim to identify clusters of objectively functionally impaired individuals based on 5R-STS and unsupervised machine learning (ML). METHODS: Data from two prospective cohort studies on patients with surgery for degenerative lumbar disease and 5R-STS times of ≥ 10.5 s-indicating presence of OFI. K-means clustering-an unsupervised ML algorithm-was applied to identify clusters of OFI. Cluster hallmarks were then identified using descriptive and inferential statistical analyses. RESULTS: We included 173 patients (mean age [standard deviation]: 46.7 [12.7] years, 45% male) and identified three types of OFI. OFI Type 1 (57 pts., 32.9%), Type 2 (81 pts., 46.8%), and Type 3 (35 pts., 20.2%) exhibited mean 5R-STS test times of 14.0 (3.2), 14.5 (3.3), and 27.1 (4.4) seconds, respectively. The grades of OFI according to the validated baseline severity stratification of the 5R-STS increased significantly with each OFI type, as did extreme anxiety and depression symptoms, issues with mobility and daily activities. Types 1 and 2 are characterized by mild to moderate OFI-with female gender, lower body mass index, and less smokers as Type I hallmarks. CONCLUSIONS: Unsupervised learning techniques identified three distinct clusters of patients with OFI that may represent a more holistic clinical classification of patients with OFI than test-time stratifications alone, by accounting for individual patient characteristics.

Multicenter external validation of prediction models for clinical outcomes after spinal fusion for lumbar degenerative disease.

BACKGROUND: Clinical prediction models (CPM), such as the SCOAP-CERTAIN tool, can be utilized to enhance decision-making for lumbar spinal fusion surgery by providing quantitative estimates of outcomes, aiding surgeons in assessing potential benefits and risks for each individual patient. External validation is crucial in CPM to assess generalizability beyond the initial dataset. This ensures performance in diverse populations, reliability and real-world applicability of the results. Therefore, we externally validated the tool for predictability of improvement in oswestry disability index (ODI), back and leg pain (BP, LP). METHODS: Prospective and retrospective data from multicenter registry was obtained. As outcome measure minimum clinically important change was chosen for ODI with ≥ 15-point and ≥ 2-point reduction for numeric rating scales (NRS) for BP and LP 12 months after lumbar fusion for degenerative disease. We externally validate this tool by calculating discrimination and calibration metrics such as intercept, slope, Brier Score, expected/observed ratio, Hosmer-Lemeshow (HL), AUC, sensitivity and specificity. RESULTS: We included 1115 patients, average age 60.8 ± 12.5 years. For 12-month ODI, area-under-the-curve (AUC) was 0.70, the calibration intercept and slope were 1.01 and 0.84, respectively. For NRS BP, AUC was 0.72, with calibration intercept of 0.97 and slope of 0.87. For NRS LP, AUC was 0.70, with calibration intercept of 0.04 and slope of 0.72. Sensitivity ranged from 0.63 to 0.96, while specificity ranged from 0.15 to 0.68. Lack of fit was found for all three models based on HL testing. CONCLUSIONS: Utilizing data from a multinational registry, we externally validate the SCOAP-CERTAIN prediction tool. The model demonstrated fair discrimination and calibration of predicted probabilities, necessitating caution in applying it in clinical practice. We suggest that future CPMs focus on predicting longer-term prognosis for this patient population, emphasizing the significance of robust calibration and thorough reporting.

From molecular signatures to radiomics: tailoring neurooncological strategies through forecasting of glioma growth.

OBJECTIVE: Contemporary oncological paradigms for adjuvant treatment of low- and intermediate-grade gliomas are often guided by a limited array of parameters, overlooking the dynamic nature of the disease. The authors' aim was to develop a comprehensive multivariate glioma growth model based on multicentric data, to facilitate more individualized therapeutic strategies. METHODS: Random slope models with subject-specific random intercepts were fitted to a retrospective cohort of grade II and III gliomas from the database at Kepler University Hospital (n = 191) to predict future mean tumor diameters. Deep learning-based radiomics was used together with a comprehensive clinical dataset and evaluated on an external prospectively collected validation cohort from University Hospital Zurich (n = 9). Prediction quality was assessed via mean squared prediction error. RESULTS: A mean squared prediction error of 0.58 cm for the external validation cohort was achieved, indicating very good prognostic value. The mean ± SD time to adjuvant therapy was 28.7 ± 43.3 months and 16.1 ± 14.6 months for the training and validation cohort, respectively, with a mean of 6.2 ± 5 and 3.6 ± 0.7, respectively, for number of observations. The observed mean tumor diameter per year was 0.38 cm (95% CI 0.25-0.51) for the training cohort, and 1.02 cm (95% CI 0.78-2.82) for the validation cohort. Glioma of the superior frontal gyrus showed a higher rate of tumor growth than insular glioma. Oligodendroglioma showed less pronounced growth, anaplastic astrocytoma-unlike anaplastic oligodendroglioma-was associated with faster tumor growth. Unlike the impact of extent of resection, isocitrate dehydrogenase (IDH) had negligible influence on tumor growth. Inclusion of radiomics variables significantly enhanced the prediction performance of the random slope model used. CONCLUSIONS: The authors developed an advanced statistical model to predict tumor volumes both pre- and postoperatively, using comprehensive data prior to the initiation of adjuvant therapy. Using radiomics enhanced the precision of the prediction models. Whereas tumor extent of resection and topology emerged as influential factors in tumor growth, the IDH status did not. This study emphasizes the imperative of advanced computational methods in refining personalized low-grade glioma treatment, advocating a move beyond traditional paradigms.

A critical moment in machine learning in medicine: on reproducible and interpretable learning.

Over the past two decades, advances in computational power and data availability combined with increased accessibility to pre-trained models have led to an exponential rise in machine learning (ML) publications. While ML may have the potential to transform healthcare, this sharp increase in ML research output without focus on methodological rigor and standard reporting guidelines has fueled a reproducibility crisis. In addition, the rapidly growing complexity of these models compromises their interpretability, which currently impedes their successful and widespread clinical adoption. In medicine, where failure of such models may have severe implications for patients' health, the high requirements for accuracy, robustness, and interpretability confront ML researchers with a unique set of challenges. In this review, we discuss the semantics of reproducibility and interpretability, as well as related issues and challenges, and outline possible solutions to counteracting the "black box". To foster reproducibility, standard reporting guidelines need to be further developed and data or code sharing encouraged. Editors and reviewers may equally play a critical role by establishing high methodological standards and thus preventing the dissemination of low-quality ML publications. To foster interpretable learning, the use of simpler models more suitable for medical data can inform the clinician how results are generated based on input data. Model-agnostic explanation tools, sensitivity analysis, and hidden layer representations constitute further promising approaches to increase interpretability. Balancing model performance and interpretability are important to ensure clinical applicability. We have now reached a critical moment for ML in medicine, where addressing these issues and implementing appropriate solutions will be vital for the future evolution of the field.

Anatomical phenotyping and staging of brain tumours.

Unlike other tumours, the anatomical extent of brain tumours is not objectified and quantified through staging. Staging systems are based on understanding the anatomical sequence of tumour progression and its relationship to histopathological dedifferentiation and survival. The aim of this study was to describe the spatiotemporal phenotype of the most frequent brain tumour entities, to assess the association of anatomical tumour features with survival probability and to develop a staging system for WHO grade 2 and 3 gliomas and glioblastoma. Anatomical phenotyping was performed on a consecutive cohort of 1000 patients with first diagnosis of a primary or secondary brain tumour. Tumour probability in different topographic, phylogenetic and ontogenetic parcellation units was assessed on preoperative MRI through normalization of the relative tumour prevalence to the relative volume of the respective structure. We analysed the spatiotemporal tumour dynamics by cross-referencing preoperative against preceding and subsequent MRIs of the respective patient. The association between anatomical phenotype and outcome defined prognostically critical anatomical tumour features at diagnosis. Based on a hypothesized sequence of anatomical tumour progression, we developed a three-level staging system for WHO grade 2 and 3 gliomas and glioblastoma. This staging system was validated internally in the original cohort and externally in an independent cohort of 300 consecutive patients. While primary CNS lymphoma showed highest probability along white matter tracts, metastases enriched along terminal arterial flow areas. Neuroepithelial tumours mapped along all sectors of the ventriculocortical axis, while adjacent units were spared, consistent with a transpallial behaviour within phylo-ontogenetic radial units. Their topographic pattern correlated with morphogenetic processes of convergence and divergence of radial units during phylo- and ontogenesis. While a ventriculofugal growth dominated in neuroepithelial tumours, a gradual deviation from this neuroepithelial spatiotemporal behaviour was found with progressive histopathological dedifferentiation. The proposed three-level staging system for WHO grade 2 and 3 gliomas and glioblastoma correlated with the degree of histological dedifferentiation and proved accurate in terms of survival upon both internal and external validation. In conclusion, this study identified specific spatiotemporal phenotypes in brain tumours through topographic probability and growth pattern assessment. The association of anatomical tumour features with survival defined critical steps in the anatomical sequence of neuroepithelial tumour progression, based on which a staging system for WHO grade 2 and 3 gliomas and glioblastoma was developed and validated.

Early versus delayed mobilization after aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis of efficacy and safety.

OBJECTIVE: A central tenet of Enhanced Recovery After Surgery (ERAS) is evidence-based medicine. Survivors of aneurysmal subarachnoid hemorrhage (aSAH) constitute a fragile patient population prone to prolonged hospitalization within neurointensive care units (NICUs), prolonged immobilization, and a range of nosocomial adverse events. Potentially, well-monitored early mobilization (EM) could constitute a beneficial element of ERAS protocols in this population. Therefore, the objective was to summarize the available evidence on EM strategies in patients with aSAH. METHODS: The authors retrieved prospective and retrospective studies that reported efficacy or safety data on EM (defined as EM in the NICU starting ≤ 7 days after ictus) versus delayed mobilization (DM) (any strategy that comparatively delayed mobilization) after aSAH and were published after January 1, 2000, in PubMed/MEDLINE, Embase, and the Cochrane Library. Random-effects meta-analysis was performed. RESULTS: Ten studies analyzing 1292 patients were included for quantitative synthesis, including 1 randomized, 1 prospective nonrandomized, and 8 retrospective studies. Modified Rankin Scale scores at discharge were not different between the EM and DM groups (mean difference [MD] [95% CI] -0.86 [-2.93 to 1.20] points, p = 0.41). Hospital length of stay in days was markedly reduced in the EM group (MD [95% CI] -6.56 [-10.64 to -2.47] days, p = 0.002). Although there was a statistically significant reduction in radiological vasospasms (OR [95% CI] 0.65 [0.44-0.97], p = 0.03), the reduction in clinically relevant vasospasms was nonsignificant (OR [95% CI] 0.63 [0.31-1.26], p = 0.19). The odds of shunting were significantly lower in the EM group (OR [95% CI] 0.61 [0.39-0.95], p = 0.03). The rates of mortality, pneumonia, and thrombosis were similar among groups (p > 0.05). CONCLUSIONS: Due to a lack of high-quality studies, vastly varying protocols, and resulting statistical clinical and statistical heterogeneity, the level of evidence for recommendations regarding EM in patients with aSAH remains low. The currently available data indicated that mobilization within the first 5 days after aneurysm repair was feasible and safe without significant excessive adverse events, that neurological outcome with EM was almost certainly not worse than with prolonged immobilization, and that there was likely at least some reduction in length of hospital stay. Radiological and clinical vasospasms were not more frequent-with signals even trending toward a decrease-in patients who mobilized early. Higher-quality studies and implementation of full ERAS protocols are necessary to evaluate efficacy and safety with a higher level of evidence and to guide practical implementation through increased standardization. Clinical trial registration no.: CRD42023432828 (www.crd.york.ac.uk/prospero).

Predictors of five-repetition sit-to-stand test performance in patients with lumbar degenerative disease.

BACKGROUND: The five-repetition sit-to-stand test (5R-STS) has recently been validated as an objective measure of functional impairment in patients with lumbar degenerative disease (LDD). Knowledge of factors influencing 5R-STS performance is useful to correct for confounders, create personalized adjusted test times, and potentially identify prognostic subgroups. We evaluate factors predicting the 5R-STS performance in patients with LDD. METHODS: Patients with LDD requiring surgery were included. Each participant performed the 5R-STS and completed a questionnaire that included their age, gender, weight, height, body mass index (BMI), smoking status, education level, employment type, ability to work, analgesic drug usage, history of previous spinal surgery, and EQ5D depression and anxiety domain. Surgical indication and index level of the spinal pathology were also recorded. Predictors of 5R-STS were identified through multivariable linear regression. RESULTS: The cohort consisted of 240 patients, 47.9% being female (mean age, 47.7 ± 13.6 years). In the final multivariable model incorporating confounders, height (regression coefficient (RC), 0.08; 95% confidence interval (CI), 0.003/0.16, p = 0.042) and being an active smoker (RC, 2.44; 95%CI, 0.56/4.32, p = 0.012) were significant predictors of worse 5R-STS performance. Full ability to work (RC, - 2.39; 95%CI, - 4.39/ - 0.39, p = 0.020) was associated with a better 5R-STS performance. Age, height, surgical indication, index level of pathology, history of previous spine surgery, history of pain, analgesic drug use, employment type, and severity of anxiety and depression symptoms demonstrated confounding effect on the 5R-STS time. CONCLUSIONS: Greater height, being an active smoker, and inability to work are significant predictors of worse 5R-STS performance in patients with LDD. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03303300 and NCT03321357.

DeepEOR: automated perioperative volumetric assessment of variable grade gliomas using deep learning.

PURPOSE: Volumetric assessments, such as extent of resection (EOR) or residual tumor volume, are essential criterions in glioma resection surgery. Our goal is to develop and validate segmentation machine learning models for pre- and postoperative magnetic resonance imaging scans, allowing us to assess the percentagewise tumor reduction after intracranial surgery for gliomas. METHODS: For the development of the preoperative segmentation model (U-Net), MRI scans of 1053 patients from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2021 as well as from patients who underwent surgery at the University Hospital in Zurich were used. Subsequently, the model was evaluated on a holdout set containing 285 images from the same sources. The postoperative model was developed using 72 scans and validated on 45 scans obtained from the BraTS 2015 and Zurich dataset. Performance is evaluated using Dice Similarity score, Jaccard coefficient and Hausdorff 95%. RESULTS: We were able to achieve an overall mean Dice Similarity Score of 0.59 and 0.29 on the pre- and postoperative holdout sets, respectively. Our algorithm managed to determine correct EOR in 44.1%. CONCLUSION: Although our models are not suitable for clinical use at this point, the possible applications are vast, going from automated lesion detection to disease progression evaluation. Precise determination of EOR is a challenging task, but we managed to show that deep learning can provide fast and objective estimates.

How we do it: the Zurich Microsurgery Lab technique for placenta preparation.

BACKGROUND: Perfused placentas provide an excellent and accessible model for microvascular dissection, microsuturing and microanastomosis training - particularly in the early microsurgical learning curve. This way, a significant amount of live animals can be spared. METHOD: We present the Zurich Microsurgery Lab protocol, detailing steps for obtaining, selecting, cleaning, flushing, cannulating, and preserving human placentas - as well as microsurgical training examples - in a tried-and-true, safe, cost-effective, and high-yield fashion. CONCLUSION: Our technique enables highly realistic microsurgical training (microdissection, microvascular repair, microanastomosis) based on readily available materials. Proper handling, preparation, and preservation of the perfused placenta models is key.

Influence of gender and sexual hormones on outcomes after pituitary surgery: a systematic review and meta-analysis.

BACKGROUND: Although there is an increasing body of evidence showing gender differences in various medical domains as well as presentation and biology of pituitary adenoma (PA), gender differences regarding outcome of patients who underwent transsphenoidal resection of PA are poorly understood. The aim of this study was to identify gender differences in PA surgery. METHODS: The PubMed/MEDLINE database was searched up to April 2023 to identify eligible articles. Quality appraisal and extraction were performed in duplicate. RESULTS: A total of 40 studies including 4989 patients were included in this systematic review and meta-analysis. Our analysis showed odds ratio of postoperative biochemical remission in males vs. females of 0.83 (95% CI 0.59-1.15, P = 0.26), odds ratio of gross total resection in male vs. female patients of 0.68 (95% CI 0.34-1.39, P = 0.30), odds ratio of postoperative diabetes insipidus in male vs. female patients of 0.40 (95% CI 0.26-0.64, P < 0.0001), and a mean difference of preoperative level of prolactin in male vs. female patients of 11.62 (95% CI - 119.04-142.27, P = 0.86). CONCLUSIONS: There was a significantly higher rate of postoperative DI in female patients after endoscopic or microscopic transsphenoidal PA surgery, and although there was some data in isolated studies suggesting influence of gender on postoperative biochemical remission, rate of GTR, and preoperative prolactin levels, these findings could not be confirmed in this meta-analysis and demonstrated no statistically significant effect. Further research is needed and future studies concerning PA surgery should report their data by gender or sexual hormones and ideally further assess their impact on PA surgery.

Promotion of a neurosurgical academic journal on social media: a 1-year experience.

BACKGROUND: Social media (SoMe) use, in all of its forms, has seen massively increased throughout the past two decades, including academic publishing. Many journals have established a SoMe presence, yet the influence of promotion of scientific publications on their visibility and impact remains poorly studied. The European Journal of Neurosurgery «Acta Neurochirurgica¼ has established its SoMe presence in form of a Twitter account that regularly promotes its publications. We aim to analyze the impact of this initial SoMe campaign on various alternative metrics (altmetrics). METHODS: A retrospective analysis of all articles published in the journal Acta Neurochirurgica between May 1st, 2018, and April 30th, 2020, was performed. These articles were divided into a historical control group - containing the articles published between May 1st, 2018, and April 30th, 2019, when the SoMe campaign was not yet established - and into an intervention group. Several altmetrics were analyzed, along with website visits and PDF downloads per month. RESULTS: In total, 784 articles published during the study period, 128 (16.3%) were promoted via Twitter. During the promotion period, 29.7% of published articles were promoted. Overall, the published articles reached a mean of 31.3 ± 50.5 website visits and 17.5 ± 31.25 PDF downloads per month. Comparing the two study periods, no statistically significant differences in website visits (26.91 ± 32.87 vs. 34.90 ± 61.08, p = 0.189) and PDF downloads (17.52 ± 31.25 vs. 15.33 ± 16.07, p = 0.276) were detected. However, overall compared to non-promoted articles, promoted articles were visited (48.9 ± 95.0 vs. 29.0 ± 37.0, p = 0.005) and downloaded significantly more (25.7 ± 66.7 vs. 16.6 ± 18.0, p = 0.045) when compared to those who were not promoted during the promotion period. CONCLUSIONS: We report a 1-year initial experience with promotion of a general neurosurgical journal on Twitter. Our data suggest a clear benefit of promotion on article site visits and article downloads, although no single responsible element could be determined in terms of altmetrics. The impact of SoMe promotion on other metrics, including traditional bibliometrics such as citations and journal impact factor, remains to be determined.

Collagen-bound fibrin sealant (TachoSil®) for dural closure in cranial surgery: single-centre comparative cohort study and systematic review of the literature.

Cerebrospinal fluid (CSF) leakage is a well-known complication of craniotomies and there are several dural closure techniques. One commonly used commercial product as adjunct for dural closure is the collagen-bound fibrin sealant TachoSil®. We analysed whether the addition of TachoSil has beneficial effects on postoperative complications and outcomes. Our prospective, institutional database was retrospectively queried, and 662 patients undergoing craniotomy were included. Three hundred fifty-two were treated with dural suture alone, and in 310, TachoSil was added after primary suture. Our primary endpoint was the rate of postoperative complications associated with CSF leakage. Secondary endpoints included functional, disability and neurological outcome. Systematic review according to PRISMA guidelines was performed to identify studies comparing primary dural closure with and without additional sealants. Postoperative complications associated with CSF leakage occurred in 24 (7.74%) and 28 (7.95%) procedures with or without TachoSil, respectively (p = 0.960). Multivariate analysis confirmed no significant differences in complication rate between the two groups (aOR 0.97, 95% CI 0.53-1.80, p = 0.930). There were no significant disparities in postoperative functional, disability or neurological scores. The systematic review identified 661 and included 8 studies in the qualitative synthesis. None showed a significant superiority of additional sealants over standard technique regarding complications, rates of revision surgery or outcome. According to our findings, we summarize that routinary use of TachoSil and similar products as adjuncts to primary dural sutures after intracranial surgical procedures is safe but without clear advantage in complication avoidance or outcome. Future studies should investigate whether their use is beneficial in high-risk settings.

Overweight and smoking promote recurrent lumbar disk herniation after discectomy.

PURPOSE: Recurrent lumbar disk herniation (rLDH) following lumbar microdiscectomy is common. While several risk factors for primary LDH have been described, risk factors for rLDH have only sparsely been investigated. We evaluate the effect of Body mass index (BMI) and smoking on the incidence and timing of rLDH. METHODS: From a prospective registry, we identified all patients undergoing primary tubular microdiscectomy (tMD), with complete BMI and smoking data, and a minimum 12-month follow-up. We defined rLDH as reherniation at the same level and side requiring surgery. Overweight was defined as BMI > 25, and obesity as BMI > 30. Intergroup comparisons and age- and gender-adjusted multivariable regression were carried out. We conducted a survival analysis to assess the influence of BMI and smoking on time to reoperation. RESULTS: Of 3012 patients, 166 (5.5%) underwent re-microdiscectomy for rLDH. Smokers were reoperated more frequently (6.4% vs. 4.0%, p = 0.007). Similarly, rLDH was more frequent in obese (7.5%) and overweight (5.9%) than in normal-weight patients (3.3%, p = 0.017). Overweight smokers had the highest rLDH rate (7.6%). This effect of smoking (Odds ratio: 1.63, 96% CI: 1.12-2.36, p = 0.010) and BMI (Odds ratio: 1.09, 95% CI: 1.02-1.17, p = 0.010) persisted after controlling for age and gender. Survival analysis demonstrated that rLDH did not occur earlier in overweight patients and/or smokers. CONCLUSIONS: BMI and smoking may directly contribute to a higher risk of rLDH, but do not accelerate rLDH development. Smoking cessation and weight loss in overweight or obese patients ought to be recommended with discectomy to reduce the risk for rLDH.

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease.

BACKGROUND: Indications and outcomes in lumbar spinal fusion for degenerative disease are notoriously heterogenous. Selected subsets of patients show remarkable benefit. However, their objective identification is often difficult. Decision-making may be improved with reliable prediction of long-term outcomes for each individual patient, improving patient selection and avoiding ineffective procedures. METHODS: Clinical prediction models for long-term functional impairment [Oswestry Disability Index (ODI) or Core Outcome Measures Index (COMI)], back pain, and leg pain after lumbar fusion for degenerative disease were developed. Achievement of the minimum clinically important difference at 12 months postoperatively was defined as a reduction from baseline of at least 15 points for ODI, 2.2 points for COMI, or 2 points for pain severity. RESULTS: Models were developed and integrated into a web-app ( https://neurosurgery.shinyapps.io/fuseml/ ) based on a multinational cohort [N = 817; 42.7% male; mean (SD) age: 61.19 (12.36) years]. At external validation [N = 298; 35.6% male; mean (SD) age: 59.73 (12.64) years], areas under the curves for functional impairment [0.67, 95% confidence interval (CI): 0.59-0.74], back pain (0.72, 95%CI: 0.64-0.79), and leg pain (0.64, 95%CI: 0.54-0.73) demonstrated moderate ability to identify patients who are likely to benefit from surgery. Models demonstrated fair calibration of the predicted probabilities. CONCLUSIONS: Outcomes after lumbar spinal fusion for degenerative disease remain difficult to predict. Although assistive clinical prediction models can help in quantifying potential benefits of surgery and the externally validated FUSE-ML tool may aid in individualized risk-benefit estimation, truly impacting clinical practice in the era of "personalized medicine" necessitates more robust tools in this patient population.

Machine Intelligence in Clinical Neuroscience: Taming the Unchained Prometheus.

The democratization of machine learning (ML) through availability of open-source learning libraries, the availability of datasets in the "big data" era, increasing computing power even on mobile devices, and online training resources have both led to an explosion in applications and publications of ML in the clinical neurosciences, but has also enabled a dangerous amount of flawed analyses and cardinal methodological errors committed by benevolent authors. While powerful ML methods are nowadays available to almost anyone and can be applied after just few minutes of familiarizing oneself with these methods, that does not imply that one has mastered these techniques. This textbook for clinicians aims to demystify ML by illustrating its methodological foundations, as well as some specific applications throughout clinical neuroscience, and its limitations. While our mind can recognize, abstract, and deal with the many uncertainties in clinical practice, algorithms cannot. Algorithms must remain tools of our own mind, tools that we should be able to master, control, and apply to our advantage in an adjunctive manner. Our hope is that this book inspires and instructs physician-scientists to continue to develop the seeds that have been planted for machine intelligence in clinical neuroscience, not forgetting their inherent limitations.

Foundations of Machine Learning-Based Clinical Prediction Modeling: Part I-Introduction and General Principles.

We provide explanations on the general principles of machine learning, as well as analytical steps required for successful machine learning-based predictive modeling, which is the focus of this series. In particular, we define the terms machine learning, artificial intelligence, as well as supervised and unsupervised learning, continuing by introducing optimization, thus, the minimization of an objective error function as the central dogma of machine learning. In addition, we discuss why it is important to separate predictive and explanatory modeling, and most importantly state that a prediction model should not be used to make inferences. Lastly, we broadly describe a classical workflow for training a machine learning model, starting with data pre-processing and feature engineering and selection, continuing on with a training structure consisting of a resampling method, hyperparameter tuning, and model selection, and ending with evaluation of model discrimination and calibration as well as robust internal or external validation of the fully developed model. Methodological rigor and clarity as well as understanding of the underlying reasoning of the internal workings of a machine learning approach are required, otherwise predictive applications despite being strong analytical tools are not well accepted into the clinical routine.